Junior Group Peter Rupprecht

Unraveling neuronal plasticity with closed-loop experiments

The main purpose of the brain is to adapt our behavior to a changing environment. But how does this occur at the level of individual neurons? In other words, how does a neuron in the brain receive feedback to improve the behavior of the organism? To study these feedback loops, we develop tools to record and control the neuronal activity and behavior in mice. We employ and continually improve two-photon microscopy for calcium imaging in hippocampus, single-cell electrophysiology and closed-loop behavioral paradigms. Key components of our work are careful experimental design as well as in-depth data analysis rather than large-scale experiments or screens. We believe that such well-thought-out experiments and analyses are ideally suited to tackle the most challenging problems in neuroscience.

Peter Rupprecht studied physics and biology at the University of Bayreuth (Germany) and the ENS Lyon (France). After his diploma in physics in 2013, he worked on computational modeling of neuronal circuits with Prof. Rainer Friedrich and on optical engineering of microscopes for neuroscience with Prof. Alipasha Vaziri. In 2014, he continued as a PhD student with Rainer Friedrich in Basel, where he used optical engineering, calcium imaging, and two-photon targeted patch clamp to dissect the olfactory circuits of zebrafish. Since 2019, he has been a postdoc in the lab of Prof. Fritjof Helmchen at the University of Zürich. There, he developed deep learning methods for the interpretation of calcium imaging data and employed calcium imaging to study the role of astrocytes and neurons during behavior in mice. In 2023, he started his own research group in the Helmchen lab, supported by an SNF Ambizione fellowship.

Open positions: We can offer several potential projects for Master’s students, mostly related to data analysis. The topics will be the analysis of astrocytic calcium imaging data or the improvement of deep learning-based algorithms for spike inference (see Research Interests).